{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "synthetic_features_and_outliers.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "i5Ul3zf5QYvW",
        "jByCP8hDRZmM",
        "WvgxW0bUSC-c",
        "copyright-notice"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "copyright-notice"
      },
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "both",
        "colab_type": "code",
        "id": "copyright-notice2",
        "colab": {}
      },
      "source": [
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4f3CKqFUqL2-",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " # 合成特征和离群值"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnKgkN5fHbGy",
        "colab_type": "text"
      },
      "source": [
        " **学习目标：**\n",
        "  * 创建一个合成特征，即另外两个特征的比例\n",
        "  * 将此新特征用作线性回归模型的输入\n",
        "  * 通过识别和截取（移除）输入数据中的离群值来提高模型的有效性"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOpLo5dcHbG0",
        "colab_type": "text"
      },
      "source": [
        " 我们来回顾下之前的“使用 TensorFlow 的基本步骤”练习中的模型。\n",
        "\n",
        "首先，我们将加利福尼亚州住房数据导入 *Pandas* `DataFrame` 中："
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S8gm6BpqRRuh",
        "colab_type": "text"
      },
      "source": [
        " ## 设置"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9D8GgUovHbG0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "73dbbd48-037b-4421-d5cd-2d771e1a460e"
      },
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import math\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.metrics as metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format\n",
        "\n",
        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.cn/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
        "\n",
        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
        "    np.random.permutation(california_housing_dataframe.index))\n",
        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
        "california_housing_dataframe"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4926</th>\n",
              "      <td>-118.1</td>\n",
              "      <td>33.8</td>\n",
              "      <td>37.0</td>\n",
              "      <td>2059.0</td>\n",
              "      <td>349.0</td>\n",
              "      <td>825.0</td>\n",
              "      <td>334.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>225.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7454</th>\n",
              "      <td>-118.3</td>\n",
              "      <td>33.9</td>\n",
              "      <td>24.0</td>\n",
              "      <td>2139.0</td>\n",
              "      <td>481.0</td>\n",
              "      <td>971.0</td>\n",
              "      <td>418.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>271.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>-117.0</td>\n",
              "      <td>33.8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6890.0</td>\n",
              "      <td>1702.0</td>\n",
              "      <td>3141.0</td>\n",
              "      <td>1451.0</td>\n",
              "      <td>1.7</td>\n",
              "      <td>95.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2438</th>\n",
              "      <td>-117.6</td>\n",
              "      <td>34.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1463.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>881.0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>152.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12617</th>\n",
              "      <td>-121.7</td>\n",
              "      <td>38.6</td>\n",
              "      <td>20.0</td>\n",
              "      <td>8627.0</td>\n",
              "      <td>1516.0</td>\n",
              "      <td>4071.0</td>\n",
              "      <td>1466.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>164.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16573</th>\n",
              "      <td>-122.7</td>\n",
              "      <td>38.3</td>\n",
              "      <td>12.0</td>\n",
              "      <td>3876.0</td>\n",
              "      <td>782.0</td>\n",
              "      <td>2146.0</td>\n",
              "      <td>764.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>165.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12159</th>\n",
              "      <td>-121.5</td>\n",
              "      <td>38.6</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3994.0</td>\n",
              "      <td>635.0</td>\n",
              "      <td>1295.0</td>\n",
              "      <td>625.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>232.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15365</th>\n",
              "      <td>-122.3</td>\n",
              "      <td>37.8</td>\n",
              "      <td>49.0</td>\n",
              "      <td>135.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>6.1</td>\n",
              "      <td>75.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9726</th>\n",
              "      <td>-119.6</td>\n",
              "      <td>36.6</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1788.0</td>\n",
              "      <td>405.0</td>\n",
              "      <td>1652.0</td>\n",
              "      <td>411.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>62.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10941</th>\n",
              "      <td>-120.9</td>\n",
              "      <td>37.8</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4838.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>2460.0</td>\n",
              "      <td>923.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>142.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_income  median_house_value\n",
              "4926      -118.1      33.8  ...            4.1               225.2\n",
              "7454      -118.3      33.9  ...            4.4               271.3\n",
              "405       -117.0      33.8  ...            1.7                95.9\n",
              "2438      -117.6      34.0  ...            4.8               152.5\n",
              "12617     -121.7      38.6  ...            4.2               164.1\n",
              "...          ...       ...  ...            ...                 ...\n",
              "16573     -122.7      38.3  ...            4.1               165.4\n",
              "12159     -121.5      38.6  ...            5.1               232.5\n",
              "15365     -122.3      37.8  ...            6.1                75.0\n",
              "9726      -119.6      36.6  ...            2.7                62.4\n",
              "10941     -120.9      37.8  ...            3.6               142.7\n",
              "\n",
              "[17000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I6kNgrwCO_ms",
        "colab_type": "text"
      },
      "source": [
        " 接下来，我们将设置输入函数，并针对模型训练来定义该函数："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RpTJER9XDub",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
        "    \"\"\"Trains a linear regression model of one feature.\n",
        "  \n",
        "    Args:\n",
        "      features: pandas DataFrame of features\n",
        "      targets: pandas DataFrame of targets\n",
        "      batch_size: Size of batches to be passed to the model\n",
        "      shuffle: True or False. Whether to shuffle the data.\n",
        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
        "    Returns:\n",
        "      Tuple of (features, labels) for next data batch\n",
        "    \"\"\"\n",
        "    \n",
        "    # Convert pandas data into a dict of np arrays.\n",
        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
        " \n",
        "    # Construct a dataset, and configure batching/repeating.\n",
        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    \n",
        "    # Shuffle the data, if specified.\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(buffer_size=10000)\n",
        "    \n",
        "    # Return the next batch of data.\n",
        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgQPftrpHbG3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
        "  \"\"\"Trains a linear regression model.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: A `float`, the learning rate.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
        "      to use as input feature.\n",
        "      \n",
        "  Returns:\n",
        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
        "    after training the model.\n",
        "  \"\"\"\n",
        "  \n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "\n",
        "  my_feature = input_feature\n",
        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
        "  my_label = \"median_house_value\"\n",
        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
        "\n",
        "  # Create input functions.\n",
        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
        "  \n",
        "  # Create feature columns.\n",
        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
        "    \n",
        "  # Create a linear regressor object.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  linear_regressor = tf.estimator.LinearRegressor(\n",
        "      feature_columns=feature_columns,\n",
        "      optimizer=my_optimizer\n",
        "  )\n",
        "\n",
        "  # Set up to plot the state of our model's line each period.\n",
        "  plt.figure(figsize=(15, 6))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.title(\"Learned Line by Period\")\n",
        "  plt.ylabel(my_label)\n",
        "  plt.xlabel(my_feature)\n",
        "  sample = california_housing_dataframe.sample(n=300)\n",
        "  plt.scatter(sample[my_feature], sample[my_label])\n",
        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print(\"Training model...\")\n",
        "  print(\"RMSE (on training data):\")\n",
        "  root_mean_squared_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    linear_regressor.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period,\n",
        "    )\n",
        "    # Take a break and compute predictions.\n",
        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "    \n",
        "    # Compute loss.\n",
        "    root_mean_squared_error = math.sqrt(\n",
        "      metrics.mean_squared_error(predictions, targets))\n",
        "    # Occasionally print the current loss.\n",
        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
        "    # Finally, track the weights and biases over time.\n",
        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
        "    y_extents = np.array([0, sample[my_label].max()])\n",
        "    \n",
        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "    \n",
        "    x_extents = (y_extents - bias) / weight\n",
        "    x_extents = np.maximum(np.minimum(x_extents,\n",
        "                                      sample[my_feature].max()),\n",
        "                           sample[my_feature].min())\n",
        "    y_extents = weight * x_extents + bias\n",
        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
        "  print(\"Model training finished.\")\n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.ylabel('RMSE')\n",
        "  plt.xlabel('Periods')\n",
        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(root_mean_squared_errors)\n",
        "\n",
        "  # Create a table with calibration data.\n",
        "  calibration_data = pd.DataFrame()\n",
        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
        "  display.display(calibration_data.describe())\n",
        "\n",
        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
        "  \n",
        "  return calibration_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJ6xUNVRm-do",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " ## 任务 1：尝试合成特征\n",
        "\n",
        "`total_rooms` 和 `population` 特征都会统计指定街区的相关总计数据。\n",
        "\n",
        "但是，如果一个街区比另一个街区的人口更密集，会怎么样？我们可以创建一个合成特征（即 `total_rooms` 与 `population` 的比例）来探索街区人口密度与房屋价值中位数之间的关系。\n",
        "\n",
        "在以下单元格中，创建一个名为 `rooms_per_person` 的特征，并将其用作 `train_model()` 的 `input_feature`。\n",
        "\n",
        "通过调整学习速率，您使用这一特征可以获得的最佳效果是什么？（效果越好，回归线与数据的拟合度就越高，最终 RMSE 也会越低。）"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isONN2XK32Wo",
        "colab_type": "text"
      },
      "source": [
        " **注意**：在下面添加一些代码单元格可能有帮助，这样您就可以尝试几种不同的学习速率并比较结果。要添加新的代码单元格，请将光标悬停在该单元格中心的正下方，然后点击**代码**。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ihcVutnnu1D",
        "colab_type": "code",
        "slideshow": {
          "slide_type": "slide"
        },
        "cellView": "both",
        "colab": {
          "test": {
            "output": "ignore",
            "timeout": 600
          }
        }
      },
      "source": [
        "#\n",
        "# YOUR CODE HERE\n",
        "#\n",
        "california_housing_dataframe[\"rooms_per_person\"] =\n",
        "\n",
        "calibration_data = train_model(\n",
        "    learning_rate=0.00005,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    input_feature=\"rooms_per_person\"\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5Ul3zf5QYvW",
        "colab_type": "text"
      },
      "source": [
        " ### 解决方案\n",
        "\n",
        "点击下方即可查看解决方案。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Leaz2oYMQcBf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1099
        },
        "outputId": "913c3305-eff7-415e-b410-60f34f184e04"
      },
      "source": [
        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
        "\n",
        "calibration_data = train_model(\n",
        "    learning_rate=0.05,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    input_feature=\"rooms_per_person\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 213.20\n",
            "  period 01 : 190.88\n",
            "  period 02 : 170.67\n",
            "  period 03 : 154.17\n",
            "  period 04 : 141.06\n",
            "  period 05 : 133.85\n",
            "  period 06 : 131.38\n",
            "  period 07 : 130.85\n",
            "  period 08 : 130.99\n",
            "  period 09 : 133.60\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>205.1</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>94.5</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>46.4</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>168.0</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>201.9</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>230.7</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4508.2</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         205.1    207.3\n",
              "std           94.5    116.0\n",
              "min           46.4     15.0\n",
              "25%          168.0    119.4\n",
              "50%          201.9    180.4\n",
              "75%          230.7    265.0\n",
              "max         4508.2    500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Final RMSE (on training data): 133.60\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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WQgghhBBCCCGEiDmSsBBCCCGEEEIIIUTMkYSFEEIIIYQQQgghYo4kLIQQQgghhBBCCBFz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97ysXh5MTFRAQzo2/Ke/t351aSvLeO0k+IPqsng8Wje/2wX/XqHMGHcweVgtKfi0hoefX4TfXqG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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjQrZ8mcHFiU",
        "colab_type": "text",
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "source": [
        " ## 任务 2：识别离群值\n",
        "\n",
        "我们可以通过创建预测值与目标值的散点图来可视化模型效果。理想情况下，这些值将位于一条完全相关的对角线上。\n",
        "\n",
        "使用您在任务 1 中训练过的人均房间数模型，并使用 Pyplot 的 `scatter()` 创建预测值与目标值的散点图。\n",
        "\n",
        "您是否看到任何异常情况？通过查看 `rooms_per_person` 中值的分布情况，将这些异常情况追溯到源数据。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0BDOec4HbG_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# YOUR CODE HERE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jByCP8hDRZmM",
        "colab_type": "text"
      },
      "source": [
        " ### 解决方案\n",
        "\n",
        "点击下方即可查看解决方案。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0tiX2gdRe-S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "outputId": "fbab0069-2226-4225-9390-3f5d26f21030"
      },
      "source": [
        "plt.figure(figsize=(15, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f9b7c5b98d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OYqnses4KtD0682Ogr4B7N60K9TOIiDplzAiq2fxN0LxGG/aN3p5byvdm8e77J2fmpuyq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\nk3EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMQD0Uq3RqTX",
        "colab_type": "text"
      },
      "source": [
        " 校准数据显示，大多数散点与一条线对齐。这条线几乎是垂直的，我们稍后再讲解。现在，我们重点关注偏离这条线的点。我们注意到这些点的数量相对较少。\n",
        "\n",
        "如果我们绘制 `rooms_per_person` 的直方图，则会发现我们的输入数据中有少量离群值："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POTM8C_ER1Oc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "b87ca9c8-89ec-4778-f614-d8db2ab7075d"
      },
      "source": [
        "plt.subplot(1, 2, 2)\n",
        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9l0KYpBQu8ed",
        "colab_type": "text"
      },
      "source": [
        " ## 任务 3：截取离群值\n",
        "\n",
        "看看您能否通过将 `rooms_per_person` 的离群值设置为相对合理的最小值或最大值来进一步改进模型拟合情况。\n",
        "\n",
        "以下是一个如何将函数应用于 Pandas `Series` 的简单示例，供您参考：\n",
        "\n",
        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
        "\n",
        "上述 `clipped_feature` 没有小于 `0` 的值。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGxjRoYlHbHC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# YOUR CODE HERE"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvgxW0bUSC-c",
        "colab_type": "text"
      },
      "source": [
        " ### 解决方案\n",
        "\n",
        "点击下方即可查看解决方案。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YGNjXPaSMPV",
        "colab_type": "text"
      },
      "source": [
        " 我们在任务 2 中创建的直方图显示，大多数值都小于 `5`。我们将 `rooms_per_person` 的值截取为 5，然后绘制直方图以再次检查结果。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YyARz6gSR7Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "b9187d91-f617-4433-9f5a-d4a19fde0423"
      },
      "source": [
        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
        "\n",
        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UKQNAkjplAEhSpwwASeqUASBJnfJJYEk6iU0DPXENcNPWtSMfwzMASerU2M8AkmwFPgGc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\nMgAkqVMGgCR1ygCQpE79HzNCSOH4JiR5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vO0e1p_aSgKA",
        "colab_type": "text"
      },
      "source": [
        " 为了验证截取是否有效，我们再训练一次模型，并再次输出校准数据："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgSP2HKfSoOH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "outputId": "1c54cb3c-c591-49a5-8ff3-67880d0e98e5"
      },
      "source": [
        "calibration_data = train_model(\n",
        "    learning_rate=0.05,\n",
        "    steps=500,\n",
        "    batch_size=5,\n",
        "    input_feature=\"rooms_per_person\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 212.82\n",
            "  period 01 : 189.05\n",
            "  period 02 : 166.71\n",
            "  period 03 : 146.37\n",
            "  period 04 : 129.75\n",
            "  period 05 : 118.58\n",
            "  period 06 : 112.10\n",
            "  period 07 : 109.75\n",
            "  period 08 : 108.96\n",
            "  period 09 : 108.34\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>194.6</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>51.4</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>43.9</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>161.8</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>194.6</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>222.6</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>434.3</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         194.6    207.3\n",
              "std           51.4    116.0\n",
              "min           43.9     15.0\n",
              "25%          161.8    119.4\n",
              "50%          194.6    180.4\n",
              "75%          222.6    265.0\n",
              "max          434.3    500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Final RMSE (on training data): 108.34\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HCQsRERERERERaXGUsBARERERERGRFieuqZ/AzL4A9gERIOycG29mnYCngL7AF8DFzrnd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AYn+SPQe87RxD404ByssCtDTFuOTiGm87R3OMtqY4CxrKNcbxDKXTuYLKh47hHhAjPR/yA4ih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\nf3CeI+h++I3/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gySE-UgfSony",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "650cd242-2bb0-4c73-e6c8-c03b2b3f90e5"
      },
      "source": [
        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Cide231RPAFs6vAert72E/j9/Kfa2ijJkwlN1d7m3O1cmrY6DTzPuXhcuuBYR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\niP8PDnzgGUTq5EUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}